Abstract
Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model on Internet of Things (IoT) environment. The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition. In addition, the pre-processing of pap smear images takes place using adaptive weighted mean filtering (AWMF) technique. Moreover, sailfish optimizer with Tsallis entropy (SFO-TE) approach has been implemented for the segmentation of pap smear images. Furthermore, a deep learning based Residual Network (ResNet50) method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images. In order to showcase the improved diagnostic outcome of the BPSIC-CDF technique, a comprehensive set of simulations take place on Herlev database. The experimental results highlighted the betterment of the BPSIC-CDF technique over the recent state of art techniques interms of different performance measures.
Highlights
Cervical cancer is the most dangerous and rapidly developing cancer that affects the lives of many females globally
In order to conquer the limitation related to the computer assisted Pap smear analyses system, manual analyses of Pap smear images using machine learning (ML) and image processing methods have been presented by various authors
A deep learning based Residual Network (ResNet50) approach was implemented as a feature extractor and cascade Deep Forest model (CDF) as a classifier to determine the class labels of the input pap smear images
Summary
Cervical cancer is the most dangerous and rapidly developing cancer that affects the lives of many females globally. Regular Pap smear screening is one of the effective and successful approaches in medicinal practices for facilitating the earlier screening and detection of cervical cancers. The network is capable of directly processing the original image, avoid the requirement for difficult preprocessing of an image It integrates the 2 factors of weight sharing, pooling, and local receptive field, which significantly decrease the training parameter of neural network [7]. The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition. The pre-processing of pap smear images takes place using adaptive weighted mean filtering (AWMF) technique. A deep learning based Residual Network (ResNet50) approach was implemented as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images. In order to showcase the improved diagnostic outcome of the BPSIC-CDF technique, a comprehensive set of simulations take place on Herlev database
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